Optimal Power Flow Program for Power Systems with Wind-PV-Storage and CHP Thermal Units

Integrating wind power, photovoltaics, energy storage systems, and combined heat and power (CHP) units into power systems makes optimal power flow (OPF) calculations more complex yet more practical. Such OPF programs must account for the coupling characteristics of multiple energy sources, operational constraints, and economic objectives. The OPF solution for wind-PV-storage and CHP systems requires establishing mathematical models incorporating multiple energy forms. The core of the program involves formulating a multi-objective optimization problem, typically aiming to minimize generation costs or maximize profits. For wind-PV-storage systems, special consideration must be given to their randomness and intermittency, which can be addressed through scenario analysis or robust optimization techniques. CHP units require electro-thermal coupling models to accurately describe their combined heat and power generation characteristics. Regarding constraints, besides traditional power flow equations and generator output limits, the model must include charging/discharging constraints for energy storage systems, forecast power constraints for wind and PV generation, and electro-thermal coupling constraints for CHP units. These constraints collectively form a complex nonlinear optimization problem. Solving such problems typically requires mixed-integer nonlinear programming or decomposition algorithms. Advanced solvers like IPOPT or CPLEX can handle medium-scale problems, while large-scale systems may need specific decomposition coordination strategies. In implementation, the optimization problem can be structured using MATLAB's Optimization Toolbox with objective functions defined through fmincon for nonlinear constraints or intlinprog for integer variables. In practical applications, these OPF programs help system operators optimally dispatch wind-PV-storage and CHP units while satisfying security constraints, increasing renewable energy utilization rates, and ensuring heating demands. Key algorithmic considerations include handling the non-convex nature of CHP constraints through piecewise linearization and implementing forecasting algorithms for renewable generation uncertainty. As energy transitions progress, these programs will play increasingly important roles in multi-energy complementary systems.